Problem in converting GRU to jit

I have trained a model containing GRU. When I try to convert it to jit, I found that the outputs of the original model and jit model are different. This problem can be reconstructed by the code below. It’s very strange that other operation(like fc/conv) is ok, the output of print(model(y)-jit_model(y)) is an zero matrix whhile GRU is not.

import torch
from torch import nn

class gruModel(nn.Module):
    def __init__(self):
        super(gruModel, self).__init__()
        self.biGRU = nn.GRU(256*5, 100,  num_layers=1, bidirectional=True, batch_first=True, bias=True)
        # self.fc = nn.Linear(256*5, 200)

    def forward(self, x):
        # x = self.fc(x)
        x, _ = self.biGRU(x, torch.zeros(2, x.size(0), 100, device=x.device))
        return x

if __name__ == '__main__':
    y = torch.rand([1, 256, 1280]).cuda()
    model = gruModel()
    model = torch.nn.DataParallel(model).to(torch.device('cuda'))

    traced_script = torch.jit.trace(model.module, y)
    jit_model = torch.jit.load("")


I have found the solution…
if the model contains GRU layer, you should load the model in cpu then to the gpu.

model = torch.jit.load(model_path, map_location=torch.device("cpu"))
model = model.cuda()

Hi @lzj9072 glad you found the solution to your problem. There’s an issue out for this problem here you can follow: Problem in converting GRU to jit